Improved signal interpretation for cast iron thickness assessment based on pulsed eddy current sensing

This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracies at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.